Field of the Invention
The present invention is related to providing weather forecasts on wide geographic areas, and more particularly, to tailoring weather forecasts to critical needs of the populace in a wide geographic area.
Background Description
Weather forecasts are based on weather data collected from sensors that are located over a large geographic area or even worldwide. In forecasting the weather for a wide geographic area, the area typically is divided into smaller more manageable units by a superimposing a grid over the area. Then, the relationship of the weather data among the several units or grid locations is described in several algebraic equations, e.g., using a Finite Element Model (FEM) for the gridded area. Frequently, the FEM requires a considerable, even excessive, amount of data processing resources.
Moreover, the higher the grid resolution, the larger the number of units, the more complex the FEM equations and, correspondingly, the more data processing resources consumed in generating weather forecasts. The data processing demands may be such that, it may be infeasible to provide real time or even timely forecasts for all grid locations. This is especially troublesome when, as is commonly the case, forecast results are subject to tight delivery deadlines. What is commonly known as adaptive mesh refinement (AMR) is a type of dynamic mesh refinement that has been used to selectively provide real time forecasts.
Adaptive mesh refinement begins with a low resolution grid for an area. The weather map contains coarse-grained cells to provide rough initial forecasts. Where more detailed forecasts are necessary for certain cells, provided there is sufficient data and time available, those cells are further refined. Typically, refinement is based on quality and quantity of sensors in the area, i.e., focus is on areas with more and better sensors. B. Plale et al., “CASA and LEAD: Adaptive Cyberinfrastructure for Real-Time Multiscale Weather Forecasting,” IEEE Computer Magazine, 2006, provides an example of sensor based refinement. Unfortunately, weather for an area is an open, unstable and dynamic system and any selected grid may cause have a divergent result. With each divergent result, the grid must be redesigned.
However, there is no guarantee that the redesigned grid will converge on a solution. When redesigning the grid in sensor based refinement does arrive at a solution though, that resulting grid still focuses on weather based the sensors, i.e. sensor quality and quantity. Thus, the final grid still may not conform to where actual interest may lie, e.g., vacation areas in or out of season. The Olympics, for example, are frequently located in somewhat remote areas, such as Lake Placid, N.Y. in 1932 and 1980. In state of the art approaches the refinement criteria could either miss important areas or include unimportant areas that could be skipped entirely. Thus, sensor based refinement still may fail to arrive at an acceptable forecast when, for example, the weather could impact people's lives. So, sensor based refinement frequently requires additional work, e.g., additional solutions. Occasionally, coarse-grained cells were re-included in the forecast to cover those otherwise omitted areas, but without any real improvement over the initial forecast for those areas.
Thus, there is a need for efficiently providing weather forecasts for large areas with a dynamic and unevenly distributed population; and, more particularly for efficiently and quickly arriving at accurate weather forecasts for large populated and unpopulated areas that are directed to areas where weather may have the most impact on the population.